DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems with deep learning. deepinv accelerates deep learning research across imaging domains, enhances research reproducibility via a common modular framework of problems and algorithms, and lowers the entrance bar to new practitioners.
Read our documentation at deepinv.github.io. Check out our 5 minute quickstart tutorial, our comprehensive examples, or our User Guide.
deepinv features
- A large framework of predefined imaging operators
- Many state-of-the-art deep neural networks, including pretrained out-of-the-box reconstruction models and denoisers
- Comprehensive frameworks for plug-and-play restoration, optimization and unfolded architectures
- Training losses for inverse problems
- Sampling algorithms and diffusion models for uncertainty quantification
- A framework for building datasets for inverse problems
Install the latest stable release of deepinv:
pip install deepinvFor more information, check out the DeepInverse GitHub repo and our documentation.